Segmentation and characterization of masses in breast ultrasound images using active contour

The active contour or Snake is a computer generated curve, used to trace boundaries of images. This paper presents the application of Snake for the segmentation of masses on breast ultrasound images and the characterization of the segmented masses as malignant or benign. Initially, the Balloon Snake is chosen to segment the masses. Comparison on the masses areas segmented by the Balloon Snake is done against the areas traced by radiologist. Experimental result shows that from fifty masses tested, the Balloon Snake successfully segment the masses with accuracy of 95.71%. Then, a mass is characterized as benign or malignant using a proposed method namely the semi-automated characterization (SAC) method. The method is based on the segmented masses produced by the Balloon Snake. The criterion of angular margin is considered in characterizing the masses as malignant or benign by the SAC method. The characterization reading of a mass by the SAC method is compared with thirty sets of characterization readings of a mass by different radiologists. The comparison is made in terms of sensitivity and specificity values. Based on the values, the receiver operating characteristics (ROC) curve is plotted for each set of comparison. From the thirty sets of comparisons, it is found that the area under curve of all the thirty ROC curves are greater than 0.7. The value implies that the SAC method gives high accuracy in characterizing benign from malignant mass. Since the method is based on the segmented masses by the Balloon Snake, the value also implies that the accuracy of Balloon Snake in segmenting the images is high (95.71%)

Partitioning intensity inhomogeneity colour images via Saliency-based active contour

Partitioning or segmenting intensity inhomogeneity colour images is a challenging problem in computer vision and image shape analysis. Given an input image, the active contour model (ACM) which is formulated in variational framework is regularly used to partition objects in the image. A selective type of variational ACM approach is better than a global approach for segmenting specific target objects, which is useful for applications such as tumor segmentation or tissue classification in medical imaging. However, the existing selective ACMs yield unsatisfactory outcomes when performing the segmentation for colour (vector-valued) with intensity variations. Therefore, our new approach incorporates both local image fitting and saliency maps into a new variational selective ACM to tackle the problem. The euler-lagrange (EL) equations were presented to solve the proposed model. Thirty combinations of synthetic and medical images were tested. The visual observation and quantitative results show that the proposed model outshines the other existing models by average, with the accuracy of 2.23% more than the compared model and the Dice and Jaccard coefficients which were around 12.78% and 19.53% higher, respectively, than the compared model

No less than a women: improving breast cancer detection & diagnosis

Breasts, being the ultimate symbol of femininity, make breast cancer one of the most traumatic events any woman could ever face. Perhaps it is this sense of pride in these attributes that makes many women reluctant to discuss and share their experiences with breast cancer. Many may feel that their absolute core identity has been shaken, making them less than a woman. The fear and stigma attached to this disease are currently among the major difficulties faced by healthcare providers in convincing women to effectively manage their breast disease. It may leave women feeling isolated and as a result, withdrawing from society and even life- making them feel less than a woman. Beyond the stigma and mental anguish there is also the tremendous stress of going through a number of surgeries, chemotherapies and radiation therapies, with the risk of treatment failure and recurrence always at the back of their minds. Fortunately various studies confirm that early breast cancer detection saves lives, reduces medical treatments and costs, and ultimately, gives one hope for a better future. The availability of effective screening reduces the mortality from breast cancer by up to 50%. Most women will be lucky enough to never develop breast cancer, but for the many of those who do, their lives may be saved by advanced detection. Currently, breast cancer detected at an early stage can be treated appropriately, with most being cured. The role of a health care provider is therefore extremely important, in counselling and motivating women to overcome their fears and come forward for regular examinations. The role of a radiologist is equally important in synergizing imaging modalities towards achieving the best of medical care for the public. These are some of the ways to help and support in the management of the disease and in making the ladies feel no less than a woman. In order to reach a superior level in early detection and diagnosis of breast cancer, our research team studied various methods to overcome some of the limitations in breast imaging. These methods include Computer Aided Diagnosis techniques involving various existing imaging modalities such as mammogram, tomosynthesis, breast ultrasound, computed tomography laser mammography (CTLM) and thermography of the breast. More rewarding research on newer imaging devices includes the ultra-wide band (UWB) imaging of the breast. Recent usage of a computational model involving Monte Carlo Simulation for early breast cancer detection using wire mesh collimator gamma camera in scintimammography is also gaining interest amongst clinicians

Segmentação de massas em ultrasons peitorais usando técnicas de multiresolução

A imagem de ultrasons é uma ferramenta de diagnóstico importante e cada vez mais aplicada na deteção do cancro da mama. No entanto, este tipo de exame é, intrinsecamente, degradado por ruído e pelo baixo contraste, resultando em di culdades na deteção de massas ou nódulos e, acima de tudo, na avaliação do seu tamanho e forma. Neste sentido, as técnicas de diagnóstico assistido por computador surgem como um factor de suporte importante para a análise deste tipo de imagem. No presente trabalho, uma abordagem bifaseada para um método de segmentação de ultrasons mamários, totalmente automático, é apresentada. A primeira etapa procura realizar uma segmentação inicial da imagem, que permita a localização primária da Região de Interesse (ROI). A segunda parte foca-se na área de nida na etapa anterior, tendo como objectivo a melhoria da resolução espacial da segmentação. Na primeira etapa de segmentação, diversas técnicas de classi cação binária são aplicadas para realizar a segmentação da imagem, utilizando características multiresolução para o descriptor de pixel - ltragem FIR passa-banda e difusão não linear e curvatura scale-space de alta escala. Estas técnicas de processamento de imagem são aplicadas para a redução da in uência dos componentes de ruído inerentes aos ultrasons e, simultaneamente, recolher informação estrutural e estatística adequada para a segmentação das massas. Os dados são classi cados usando Support Vector Machines e Análise Discriminante. Na segunda fase, as máscaras obtidas a partir da segmentação inicial são dilatadas, produzindo uma área restrita que contém a ROI. Considerando apenas os pixéis pertencentes a esta região, uma nova segmentação é executada, através do algoritmo AdaBoost, usando a difusão não linear e curvaturas de menor escala. Um algoritmo de contornos activos é, também, aplicado para melhorar os resultados da segmentação, sendo as máscaras da segmentação inicial utilizadas como contornos iniciais. Os resultados nais con rmam a metodologia proposta como sendo uma solução promissora para a segmentação de massas em imagens de ultrasons da mama, revelando, em termos globais, bons resultados de acurácia - 97,58% (AdaBoost) e 97,70% (Contornos Activos) -, sensibilidade - 76,46% (AdaBoost) e 75,40% (Contornos Activos) - e de precisão - 87,26% (AdaBoost) e 87,51% (Contornos Activos).Breast ultrasound imaging is an important and increasingly applied diagnostic tool for breast cancer detection. However, this type of exam is intrinsically degraded by noise, resulting in a dif cult detection of masses or nodules, and, most importantly, the evaluation of their size and shape. Computer-aided diagnosis arises as a major help factor, for the analysis of this type of medical imaging. In this work, a two-stage approach towards a fully automated BUS segmentation method is presented. The rst stage attempts an initial segmentation of the BUS image, used to track the ROI. The second part focuses on the area surrounding the ROI de ned in the rst stage, improving the spatial resolution of the segmentation. In the rst segmentation stage, several binary class cation techniques are applied to perform image segmentation, using multi-resolution features to construct the pixel descriptor - FIR bandpass ltering and high scale non-linear diffusion and scale-space curvature. These processing techniques were chosen to reduce the in uence of noise components that are inherent to ultrasound images and, simultaneously, select structural and statistical information suitable for the segmentation of masses. The data is classi ed using Support Vector Machines and Discriminant Analysis. In the second stage, the masks obtained from the initial segmentation are dilated, yielding a restricted area containing the ROI. Considering only the pixels inside this region, a new segmentation task is performed. The images are classifed using an AdaBoost classi er, using lower scale non-linear diffusion and scale-space curvature measures. Active contours are also used to improve the segmentation results, being the initial segmentation masks are used as initial contours. Final results con rm the proposed methods as a promising solution for mass segmentation in BUS images, achieving good overall accuracy - 97.58% for (AdaBoost) and 97.70% (Active Contours) -, recall - 76.46% (AdaBoost) and 75.40% (Active Contours) - and precision - 87.26% (AdaBoost) and 87.51% (Active Contours) - results~